Triple

T13345456
Position Surface form Disambiguated ID Type / Status
Subject Segrià E317936 entity
Predicate hasMunicipality P847 FINISHED
Object Torres de Segre
Torres de Segre is a municipality in the comarca of Segrià in the province of Lleida, Catalonia, Spain.
E1035324 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Torres de Segre | Statement: [Segrià, hasMunicipality, Torres de Segre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Torres de Segre
Context triple: [Segrià, hasMunicipality, Torres de Segre]
  • A. Gran Tarajal
    Gran Tarajal is a coastal town and important fishing and commercial port on the island of Fuerteventura in Spain’s Canary Islands.
  • B. Torres de Serranos
    Torres de Serranos is a large medieval gate and one of the best-preserved remnants of Valencia’s old city walls, serving today as an iconic symbol of the city.
  • C. Ronda de Dalt
    Ronda de Dalt is a major ring road in the Barcelona metropolitan area that helps divert and distribute traffic around the city.
  • D. Garraf
    Garraf is a coastal comarca in Catalonia, Spain, known for its Mediterranean landscapes, natural park, and seaside towns such as Sitges and Vilanova i la Geltrú.
  • E. La Massana
    La Massana is a parish in northwestern Andorra known for its mountainous landscape, ski resorts, and outdoor tourism.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Torres de Segre
Triple: [Segrià, hasMunicipality, Torres de Segre]
Generated description
Torres de Segre is a municipality in the comarca of Segrià in the province of Lleida, Catalonia, Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Torres de Segre
Target entity description: Torres de Segre is a municipality in the comarca of Segrià in the province of Lleida, Catalonia, Spain.
  • A. Gran Tarajal
    Gran Tarajal is a coastal town and important fishing and commercial port on the island of Fuerteventura in Spain’s Canary Islands.
  • B. Torres de Serranos
    Torres de Serranos is a large medieval gate and one of the best-preserved remnants of Valencia’s old city walls, serving today as an iconic symbol of the city.
  • C. Ronda de Dalt
    Ronda de Dalt is a major ring road in the Barcelona metropolitan area that helps divert and distribute traffic around the city.
  • D. Garraf
    Garraf is a coastal comarca in Catalonia, Spain, known for its Mediterranean landscapes, natural park, and seaside towns such as Sitges and Vilanova i la Geltrú.
  • E. La Massana
    La Massana is a parish in northwestern Andorra known for its mountainous landscape, ski resorts, and outdoor tourism.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d806b5a3c08190b42c267fb092f98a completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d99e89c65c819093f3bea11d6073c5 completed April 11, 2026, 1:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69f71f439b3c8190b35fd4d097d65068 completed May 3, 2026, 10:11 a.m.
NEDg Description generation batch_69f7204ac36c8190a04e921442489e9c completed May 3, 2026, 10:15 a.m.
NED2 Entity disambiguation (via description) batch_69f7221af9e881908b65ab2e7aec0c78 completed May 3, 2026, 10:23 a.m.
Created at: April 9, 2026, 9:31 p.m.